Over the past 10 years we have developed and distributed a morphometry package for automatically characterizing and quantifying neuroanatomical structures in the human brain, including the automated construction of models of the hippocampus, amygdala, ventricular system and neocortex. These tools have been designed for use in a research setting, and have deepened our understanding of a wide variety of neurological disorders and effects such as schizophrenia, Huntington's disease, Alzheimer's disease, Semantic Dementia, phobias, autism, dyslexia, aging, and development. Unfortunately, design decisions made over a decade ago now hamper the adoption of these tools into clinical settings. In this project we propose to use state-of-the art software engineering methods in order to remove these restrictions by designing and engineering these algorithms using current graphics processor unit (GPU) technology, which has been shown to routinely provide on the order of 50-fold speed increases. An additional advantage will be the careful unit testing that can be built into the new tools, ensuring they operate with the high degree of accuracy and reliability required for point-of-care clinical tools. The result will be a suite of open-source algorithms that can run on a standard workstation with a commercially available graphics card, which can rapidly provide diagnostic information for multiple conditions at the point-of-care. PUBLIC HEALTH RELEVANCE: Neurodegenerative disorders result in different patterns of atrophy in the human brain, with each pattern giving rise to the characteristic clinical impairments typically used for diagnosis. Unfortunately, the behavioral changes typically used for diagnosis can be ambiguous, making neuroimaging a potentially valuable diagnostic tool. The goal of this project is to fill this unmet need, by providing a set of tools for quantifying neuroanatomical properties of the human brain from routine clinical MRI scans. Using currently available low- cost graphics cards we will be able to analyze this type of data on standard workstations resulting in information that can be used in diagnosing a wide array of neurological disorders by quantifying the size and shape of structures such as the hippocampus, the amygdala, the ventricular system and the neocortex. We will provide access to statistical information regarding normal variability in these structures, resulting in a tool that can automatically, rapidly and robustly detect abnormal brain anatomy indicative of disease process at the point-of-care.